15-441 Computer Networking

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15-441 Computer Networking

• Lecture 22 Queue Management and QoS

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Overview

• Queue management RED
• Fair-queuing
• Why QOS?
• Integrated services

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Queuing Disciplines

• Each router must implement some queuing
  discipline
• Queuing allocates both bandwidth and buffer
  space
• Bandwidth which packet to serve (transmit) next
• Buffer space which packet to drop next (when
  required)
• Queuing also affects latency

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Typical Internet Queuing

• FIFO drop-tail
• Simplest choice
• Used widely in the Internet
• FIFO (first-in-first-out)
• Implies single class of traffic
• Drop-tail
• Arriving packets get dropped when queue is full
  regardless of flow or importance
• Important distinction
• FIFO scheduling discipline
• Drop-tail drop policy

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FIFO Drop-tail Problems

• Leaves responsibility of congestion control
  completely to the edges (e.g., TCP)
• Does not separate between different flows
• No policing send more packets ? get more service
• Synchronization end hosts react to same events

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FIFO Drop-tail Problems

• Full queues
• Routers are forced to have have large queues to
  maintain high utilizations
• TCP detects congestion from loss
• Forces network to have long standing queues in
  steady-state
• Lock-out problem
• Drop-tail routers treat bursty traffic poorly
• Traffic gets synchronized easily ? allows a few
  flows to monopolize the queue space

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Active Queue Management

• Design active router queue management to aid
  congestion control
• Why?
• Router has unified view of queuing behavior
• Routers see actual queue occupancy (distinguish
  queue delay and propagation delay)
• Routers can decide on transient congestion, based
  on workload

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Design Objectives

• Keep throughput high and delay low
• High power (throughput/delay)
• Accommodate bursts
• Queue size should reflect ability to accept
  bursts rather than steady-state queuing
• Improve TCP performance with minimal hardware
  changes

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Lock-out Problem

• Random drop
• Packet arriving when queue is full causes some
  random packet to be dropped
• Drop front
• On full queue, drop packet at head of queue
• Random drop and drop front solve the lock-out
  problem but not the full-queues problem

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Full Queues Problem

• Drop packets before queue becomes full (early
  drop)
• Intuition notify senders of incipient congestion
• Example early random drop (ERD)
• If qlen gt drop level, drop each new packet with
  fixed probability p
• Does not control misbehaving users

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Random Early Detection (RED)

• Detect incipient congestion
• Assume hosts respond to lost packets
• Avoid window synchronization
• Randomly mark packets
• Avoid bias against bursty traffic

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RED Algorithm

• Maintain running average of queue length
• If avg lt minth do nothing
• Low queuing, send packets through
• If avg gt maxth, drop packet
• Protection from misbehaving sources
• Else mark packet in a manner proportional to
  queue length
• Notify sources of incipient congestion

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RED Operation
Min thresh
Max thresh
Average Queue Length
P(drop)
1.0
maxP
minth
maxth
Avg queue length
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Overview

• Queue management RED
• Fair-queuing
• Why QOS?
• Integrated services

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Fairness Goals

• Allocate resources fairly
• Isolate ill-behaved users
• Router does not send explicit feedback to source
• Still needs e2e congestion control
• Still achieve statistical muxing
• One flow can fill entire pipe if no contenders
• Work conserving ? scheduler never idles link if
  it has a packet

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What is Fairness?

• At what granularity?
• Flows, connections, domains?
• What if users have different RTTs/links/etc.
• Should it share a link fairly or be TCP fair?
• Maximize fairness index?
• Fairness (Sxi)2/n(Sxi2) 0ltfairnesslt1
• Basically a tough question to answer typically
  design mechanisms instead of policy
• User arbitrary granularity

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Max-min Fairness

• Allocate user with small demand what it wants,
  evenly divide unused resources to big users
• Formally
• Resources allocated in terms of increasing demand
• No source gets resource share larger than its
  demand
• Sources with unsatisfied demands get equal share
  of resource

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Implementing Max-min Fairness

• Generalized processor sharing
• Fluid fairness
• Bitwise round robin among all queues
• Why not simple round robin?
• Variable packet length ? can get more service by
  sending bigger packets
• Unfair instantaneous service rate
• What if arrive just before/after packet departs?

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Bit-by-bit RR

• Single flow clock ticks when a bit is
  transmitted. For packet i
• Pi length, Ai arrival time, Si begin
  transmit time, Fi finish transmit time
• Fi SiPi max (Fi-1, Ai) Pi
• Multiple flows clock ticks when a bit from all
  active flows is transmitted ? round number
• Can calculate Fi for each packet if number of
  flows is know at all times
• Why do we need to know flow count? ? need to know
  A ? This can be complicated

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Bit-by-bit RR Illustration

• Not feasible to interleave bits on real networks
• FQ simulates bit-by-bit RR

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Fair Queuing

• Mapping bit-by-bit schedule onto packet
  transmission schedule
• Transmit packet with the lowest Fi at any given
  time
• How do you compute Fi?

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FQ Illustration
Flow 1
Flow 2
I/P
O/P
Flow n
Variation Weighted Fair Queuing (WFQ)
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Bit-by-bit RR Example
Flow 1
Flow 2
F10
F8
F5
Cannot preempt packet currently being transmitted
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Fair Queuing Tradeoffs

• FQ can control congestion by monitoring flows
• Non-adaptive flows can still be a problem why?
• Complex state
• Must keep queue per flow
• Hard in routers with many flows (e.g., backbone
  routers)
• Flow aggregation is a possibility (e.g. do
  fairness per domain)
• Complex computation
• Classification into flows may be hard
• Must keep queues sorted by finish times
• dR/dt changes whenever the flow count changes

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Overview

• Queue management RED
• Fair-queuing
• Why QOS?
• Integrated services

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Motivation

• Internet currently provides one single class of
  best-effort service
• No assurances about delivery
• Existing applications are elastic
• Tolerate delays and losses
• Can adapt to congestion
• Future real-time applications may be inelastic

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Why a New Service Model?

• What is the basic objective of network design?
• Maximize total bandwidth? Minimize latency?
• Maximize user satisfaction the total utility
  given to users
• What does utility vs. bandwidth look like?
• Must be non-decreasing function
• Shape depends on application

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Utility Curve Shapes
Stay to the right and you are fine for all curves
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Utility curve Elastic traffic
U
Elastic
Bandwidth
Does equal allocation of bandwidth maximize total
utility?
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Admission Control

• If U(bandwidth) is concave
• ? elastic applications
• Incremental utility is decreasing with increasing
  bandwidth
• Is always advantageous to have more flows with
  lower bandwidth
• No need of admission control
• This is why the Internet works!

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Utility Curves Inelastic traffic
Does equal allocation of bandwidth maximize total
utility?
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Inelastic Applications

• Continuous media applications
• Lower and upper limit on acceptable performance.
• BW below which video and audio are not
  intelligible
• Internet telephones, teleconferencing with high
  delay (200 - 300ms) impair human interaction
• Sometimes called tolerant real-time since they
  can adapt to the performance of the network
• Hard real-time applications
• Require hard limits on performance
• E.g. control applications

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Admission Control

• If U is convex ? inelastic applications
• U(number of flows) is no longer monotonically
  increasing
• Need admission control to maximize total utility
• Admission control ? deciding when adding more
  people would reduce overall utility
• Basically avoids overload

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Overview

• Queue management RED
• Fair-queuing
• Why QOS?
• Integrated services

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Components of Integrated Services

• Type of commitment
• What does the network promise?
• Packet scheduling
• How does the network meet promises?
• Service interface
• How does the application describe what it
  wants?
• Establishing the guarantee
• How is the promise communicated to/from the
  network
• How is admission of new applications
  controlled?

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Type of Commitments

• Guaranteed service
• For hard real-time applications
• Fixed guarantee, network meets commitment if
  clients send at agreed-upon rate
• Predicted service
• For delay-adaptive applications
• Two components
• If conditions do not change, commit to current
  service
• If conditions change, take steps to deliver
  consistent performance (help apps minimize
  playback delay)
• Implicit assumption network does not change
  much over time
• Datagram/best effort service

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Scheduling for Guaranteed Traffic

• Use token bucket filter to characterize traffic
• Described by rate r and bucket depth b
• Use Weighted Fair-Queueing at the routers
• Parekhs bound for worst case queuing delay b/r

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Token Bucket Filter
Tokens enter bucket at rate r

• Operation
• If bucket fills, tokens are discarded
• Sending a packet of size P uses P tokens
• If bucket has P tokens, packet sent at max rate,
  else must wait for tokens to accumulate

Bucket depth b capacity of bucket
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Token Bucket Operation
Tokens
Tokens
Tokens
Overflow
Packet
Packet
Not enough tokens ? wait for tokens to accumulate
Enough tokens ? packet goes through, tokens
removed
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Token Bucket Characteristics

• On the long run, rate is limited to r
• On the short run, a burst of size b can be sent
• Amount of traffic entering at interval T is
  bounded by
• Traffic b rT
• Information useful to admission algorithm

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Token Bucket Specs
BW
Flow B
2
Flow A r 1 MBps, B1 byte
1
Flow A
Flow B r 1 MBps, B1MB
1
2
3
Time
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Guarantee Proven by Parekh

• Given
• Flow i shaped with token bucket and leaky bucket
  rate control (depth b and rate r)
• Network nodes do WFQ
• Cumulative queuing delay Di suffered by flow i
  has upper bound
• Di lt b/r, (where r may be much larger than
  average rate)
• Assumes that ?r lt link speed at any router
• All sources limiting themselves to r will result
  in no network queuing

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Sharing versus Isolation

• Isolation
• Isolates well-behaved from misbehaving sources
• Sharing
• Mixing of different sources in a way beneficial
  to all
• FIFO sharing
• each traffic source impacts other connections
  directly
• e.g. malicious user can grab extra bandwidth
• the simplest and most common queueing discipline
• averages out the delay across all flows
• Priority queues one-way sharing
• high-priority traffic sources have impact on
  lower priority traffic only
• has to be combined with admission control and
  traffic enforcement to avoid starvation of
  low-priority traffic
• WFQ two-way isolation
• provides a guaranteed minimum throughput (and
  maximum delay)

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Putting It All Together

• Assume 3 types of traffic guaranteed,
  predictive, best-effort
• Scheduling use WFQ in routers
• Each guaranteed flow gets its own queue
• All predicted service flows and best effort
  aggregates in single separate queue
• Predictive traffic classes
• Worst case delay for classes separated by order
  of magnitude
• When high priority needs extra bandwidth steals
  it from lower class
• Best effort traffic acts as lowest priority class

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Service Interfaces

• Guaranteed Traffic
• Host specifies rate to network
• Why not bucket size b?
• If delay not good, ask for higher rate
• Predicted Traffic
• Specifies (r, b) token bucket parameters
• Specifies delay D and loss rate L
• Network assigns priority class
• Policing at edges to drop or tag packets
• Needed to provide isolation why is this not
  done for guaranteed traffic?
• WFQ provides this for guaranteed traffic

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Lessons

• TCP can use help from routers
• RED ? eliminate lock-out and full-queues problems
• FQ ? heavy-weight but explicitly fair to all
• QoS
• What type of applications are there? ? Elastic,
  hard real-time and adaptive real-time
• Why do we need admission control ? to maximize
  utility
• How do token buckets WFQ provide QoS
  guarantees?

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EXTRA SLIDES

• The rest of the slides are FYI

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Max-min Fairness Example

• Assume sources 1..n, with resource demands X1..Xn
  in ascending order
• Assume channel capacity C.
• Give C/n to X1 if this is more than X1 wants,
  divide excess (C/n - X1) to other sources each
  gets C/n (C/n - X1)/(n-1)
• If this is larger than what X2 wants, repeat
  process

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Predicted Service

• FIFO jitter increases with the number of hops
• Use opportunity for sharing across hops
• FIFO
• At each hop measure average delay for class at
  that router
• For each packet compute difference of average
  delay and delay of that packet in queue
• Add/subtract difference in packet header
• Packet inserted into queues expected arrival time
  instead of actual
• More complex queue management!
• Slightly decreases mean delay and significantly
  decreases jitter

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Possible Token Bucket Uses

• Shaping, policing, marking
• Delay pkts from entering net (shaping)
• Drop pkts that arrive without tokens (policing)
• Let all pkts pass through, mark ones without
  tokens
• Network drops pkts without tokens in time of
  congestion

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Applications Variations

• Rigid adaptive applications
• Rigid set fixed playback point
• Adaptive adapt playback point
• Gamble that network conditions will be the same
  as in the past
• Are prepared to deal with errors in their
  estimate
• Will have an earlier playback point than rigid
  applications
• Tolerant intolerant applications
• Tolerance to brief interruptions in service
• 4 combinations

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Applications Variations

• Really only two classes of applications
• 1) Intolerant and rigid
• 2) Tolerant and adaptive
• Other combinations make little sense
• 3) Intolerant and adaptive
• - Cannot adapt without interruption
• Tolerant and rigid
• - Missed opportunity to improve delay
• So what service classes should the
• network offer?